基于梯度耦合自编码器与全连接网络的桥梁传感器数据重构与结构损伤识别OA

A dual-task parallel machine learning framework was developed by integrating a convolutional autoencoder(CAE)and a fully connected neural network(FCNN)via the gradient-coupled mechanism,enabling simultaneous data compression-reconstruction and structural damage iden-tification.Under the condition where 40%of the sensor nodes are missing,the model successfully reconstructs the full sensor network with an R² of 0.916 and normalized root mean square error(NRMSE)of 0.028 8.Even under sig-nificant noise contamination with an SNR of 12 dB,the model maintains strong reconstruction performance,achiev-ing a R² of 0.910 and NRMSE of 0.025 3.Forty-six struc-tural damage scenarios were simulated using the scaled bridge model.The accuracy of spatial localization and quan-tification of the damage severity using the framework ex-ceeds 99.3%.The proposed framework reduces the training time by 54.4%and iteration counts by 45.5%compared to conventional two-stage machine learning approaches,dem-onstrating the efficiency of gradient-coupled optimization.